In power system， the trust evaluation of terminals is a key technology to grade the access and securely collect data， which is critical to ensure the safe and stable operation of the power grid. Traditional trust evaluation models usually calculate the trust score directly based on identification， running states and interaction histories， etc. of the terminals， and show poor performance with indirect attacks and node collusion. To address these problems， an Adaptive Interaction Feedback based Trust evaluation （AIFTrust） mechanism was proposed. In the proposed mechanism， device trust level was measured comprehensively based on direct trust evaluation module， trust recommendation module and trust aggregation module， and accurate trust evaluation for massive collaborative terminals in power information systems was achieved. First， the interaction cost was introduced by the direct trust evaluation module， and the direct trust score of the malicious target terminal was calculated on the basis of the trust decay policy. Then， the experience similarity was introduced by the trust recommendation evaluation module， and similar terminals were recommended through secondary clustering to improve the reliability of the recommendation trust scoring. After the above， the trust aggregation module was used to adaptively aggregate the direct trust score and the recommendation trust score based on the trust score accuracy. Simulation results on real datasets and synthetic datasets show that when attack probability is 30% and trust decay rate is 0.05， AIFTrust improves the recommendation accuracy by 13.30% and 14.81% compared to the similarity-based trust evaluation method SFM （Similarity FraMework） and the trust evaluation method based on objective information entropy CRT （Reputation Trusted based on Cooperation）， respectively.